In order to enable an iCal export link, your account needs to have a key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.

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In conjunction with a having a key associated with your account, to have the possibility of exporting private event information necessitates the creation of a persistent key. This new key is also associated with your account and whilst it is active the data which can be obtained through using this key can be obtained by anyone in possession of the link provided. Due to this reason, it is extremely important that you keep links generated with this key private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately remove it from 'My Profile' under the 'HTTP API' tab and generate a new key before regenerating iCalendar links.

The “School on Data Combination and Limit Setting” focuses on two topics of immense importance for the LHC: For many physics analyses, several channels are combined into one result. In a similar way results from different experiments are merged. The aim of this procedure is to increase the precision (in case of measurements of observables) or to arrive at stricter limits in the case of searches for new physics.
The school will introduce the basic concepts for data combination and limit setting and will deepen their understanding with practical examples, both educational and from real physics analyses. Furthermore, relevant tools will be shown. Discussions with experts complete the programme.

The series of lectures will cover the statistical methods used in searches for new phenomena in a particle physics experiment. Statistical tests will be formally defined and used to quantify the level of agreement between a specified model and the observed data. Specifically, one tries to reject the Standard Model in such a test, as this will indicate the discovery of something new. Even in the absence of a discovery, we would like to say what possible signal models one may exclude by setting limits on their parameters. Several procedures for doing this will be discussed, including CLs, Power-Constrained Limits (PCL), Bayesian, and Feldman-Cousins methods. The lectures will focus on frequentist methods, but the Bayesian approach will be addressed as well. In both cases the role of systematic uncertainties will be emphasized. Computer tutorials will provide a practical exposure to the procedures covered in the lectures.